Integration of multi-classifiers in object-based methods for forest classification in the Loess plateau, China

ABSTRACT: The object-oriented method with three integrated different classifiers was applied to classify satellite images of the forest in the Loess Plateau in China. After image segmentation, feature selection, and training sample selection, three classifiers–-the support vector machine (SVM), k-nearest neighbour algorithm, and classification and regression tree–-were used for forest classification using SPOT images as the data source. Results indicated that the object-oriented method with the three classifiers effectively extracted Chinese pine forestland, Betula forestland, oak forestland, shrubland, wasteland, farmland, and roads in the study area. The main segmentation parameters of scale, colour, and shape performed best when their values were set to 100, 0.9, 0.1 in forestland and 60, 0.5, 0.5 in non-forestland, respectively. In addition, SVM was the best classifier applied to the forest classification with an overall accuracy of 78% and a kappa coefficient of 0.737. This study provides a fast and flexible approach to forest classification and lays the foundation for forest management and forest resource surveys.